How Important Is POS to Dependency Parsing? Joint POS Tagging and Dependency Parsing Neural Networks

Author:

Lu Hsuehkuan,Hou Lei,Li Juanzi

Publisher

Springer International Publishing

Reference29 articles.

1. Andor, D., et al.: Globally normalized transition-based neural networks. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2442–2452. Association for Computational Linguistics (2016)

2. Ballesteros, M., Dyer, C., Smith, N.A.: Improved transition-based parsing by modeling characters instead of words with LSTMs. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 349–359. Association for Computational Linguistics (2015)

3. Bohnet, B.: Top accuracy and fast dependency parsing is not a contradiction. In: Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), pp. 89–97. Coling 2010 Organizing Committee (2010)

4. Bohnet, B., Nivre, J.: A transition-based system for joint part-of-speech tagging and labeled non-projective dependency parsing. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1455–1465. Association for Computational Linguistics (2012)

5. Bunescu, R.C., Mooney, R.J.: A shortest path dependency kernel for relation extraction. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 724–731. Association for Computational Linguistics (2005)

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